Asset-liability management (ALM) denotes the adaptation of the portfolio-management process in order to handle the presence of various constraints relating to the commitments that represent the liabilities of an investor. Academic research has suggested that suitable extensions of portfolio-optimisation techniques used by institutional investors (for example, pension funds) could be usefully transposed to the context of private wealth management precisely because they have been engineered to allow for the incorporation in the portfolio-construction process of investors’ specific constraints, objectives and horizons, all of which can possibly be summarised in terms of a single state variable – the value of the “liability” portfolio.

Within the framework of private wealth management, we use a broad definition of “liabilities” which encompasses any commitment or spending objective, typically self-imposed, that investors are facing. It is not the performance of a particular fund or asset class that will determine the ability to meet private investor’s expectations. The success or failure of the satisfaction of the investor’s long-term objectives is fundamentally dependent on an ALM exercise that aims at determining the proper strategic inter-class allocation as a function of the investor’s specific objectives and constraints, in addition to the investor’s time-horizon. What will prove to be decisive is the ability to design an asset-allocation solution that is a function of the risks to which the investor is exposed, as opposed to the market as a whole.

Unfamiliar rather than sceptical

In a recent survey of best practices in private wealth management, Amenc et al find that private asset-liability management is perceived as a promising concept for integrating client-specific objectives but is still underused. Private wealth managers who are unfamiliar with ALM and those who are familiar with it but do not use it make up the majority of the respondents to the survey. A large majority of those familiar with ALM techniques but do not currently use them actually consider them useful, so the failure to adopt ALM in private wealth management as widely as in institutional investment management has more to do with unfamiliarity with the concept and with the perceived difficulty of using it than with sceptical views on its usefulness. Only 57 per cent of the 52 per cent of respondents who are familiar with ALM actually use it to allocate assets – a figure that means, overall, only one-third of respondents actually use ALM. Private wealth managers focus mainly on general inflation rather than on inflation specific to client objectives, so ALM is largely seen as a way of hedging against “typical” liabilities; optimal practice would, in principle, emphasise investor-specific liabilities in the context of a fully customised approach.

Three key paradigms

The perfect solution for ultra-high-net-worth clients and large family offices is a highly customised approach. However, such methods cannot be implemented for all private investors. It is therefore appropriate for the asset management industry to work towards the design of semi-customised long-term investing strategies that can allow for the incorporation of a class of private investors’ horizons and objectives. Currently available target-date fund products, mostly oriented towards retail clients, are not a satisfactory answer to the problem because they are based on simplistic allocation schemes leading to a deterministic decrease in equity allocation, regardless of market conditions and the current status of investors’ risk budgets.

We argue that improved long-term investing strategies can be designed for the private wealth management industry. These dynamic asset-allocation benchmarks are based on an industrialisation of three key paradigms that have recently emerged in institutional money management: liability-driven investing (LDI) to take into account private clients’ consumption objectives; lifecycle investing (LCI) that recognises clients’ horizons; and risk-control investing (RCI) that accounts for private clients’ risk budgets. Implementing such optimal strategies in a delegated-money management context is a serious challenge, which requires a narrower classification of private investors based on factors other than their age and risk aversion.

Allowing for different allocation strategies

The challenge is to design a parsimonious partition of the investors/states of nature that will allow for different allocation strategies. There are two sets of attributes that should be used to define the various categories of asset allocation decisions: subjective and objective attributes. The subjective attributes are related to each particular investor, and include, in addition to age, which is the sole determinant in current target-date fund products, risk aversion and investor liabilities. Martellini and Milhau propose a partition of the age of investors and show that the opportunity cost of partitioning remains low.

In our research, we analyse the opportunity cost related to objective attributes, which implies that asset allocation decisions will be a function of the following two state variables: the current (estimated) level of risk premium (typically proxied by a function of dividend yield or price-earning ratios) and the current volatility level. A first attempt to extend deterministic target-date-fund products was proposed by Lewis and Okunev, who design allocations that depend on current value-at-risk estimates. Typically, a discrete partition of the states of the world can be used for the equity risk premium and equity volatility, with suitably defined high, median and low values.

One key element missing from the analysis presented so far, which can be usefully introduced in a private wealth management context, is the incorporation of short-term constraints to the design of allocation strategies. Most private investors, even those with very long horizons,typically face a number of short-term performance constraints, particularly maximum drawdown constraints. These constraints are managed not through diversification strategies, which are dedicated to the design of the performance-seeking portfolio, or hedging strategies, which are dedicated to immunising the portfolio value against changes in key risk factors, but through insurance strategies, which are designed to limit the portfolio losses during significant financial turmoil. The practical implication of the introduction of short-term constraints is that optimal investment in a performance-seeking satellite portfolio is a function not only of risk aversion but also of risk budgets. A pre-commitment to risk management allows risk exposure to be adjusted in an optimal statedependent manner and therefore generates the highest exposure to the upside potential of the PSP while respecting risk constraints.

A series of dedicated long-term allocation benchmarks

Our research shows that the implementation of optimal long-term investing strategies in a delegated money management context is possible using a parsimonious partition of market conditions. More precisely, we show that strategies based on such partitions are much closer to the truly optimal strategy in terms of expected utility than deterministic allocation schemes. On the technical front, our research complements Munk et al, and Sangvinatsos and Wachter by deriving a quasi-analytical representation of the optimal portfolio in the presence of stochastic volatility and unspanned-equity risk premium. Having quasi-analytical expressions for the optimal strategy turns out to be very useful in the analysis of the sub-optimality of allocation strategies embedded within target-date funds. We confirm that for reasonable parameter values, the opportunity cost involved in focusing on a deterministic allocation scheme is very substantial.

Our results also show that the constraints related to limited customisation – inherent to the privat-wealth management context – can be handled through the development of a series of dedicated long-term allocation benchmarks, as the natural way to implement ALM solutions for private clients.

These long-term investing strategies are very good approximations for truly optimal allocation strategies that are designed to maximise the probability to achieve the client’s long-term objectives while meeting their short-term constraints. They also imply an entirely new mode of relationship with the clients, based on a focus on the client’s needs, as opposed to a focus on a particular product’s performance in a particular sample period.

Noël Amenc is a professor of finance at EDHEC Business School and director of EDHEC-Risk Institute. Romain Deguest is a senior research engineer at the institute. Lionel Martellini is a professor of finance at the business school and scientific director at the institute. Vincent Milhau is scientific director at the institute.

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